[Reproducible-builds] Bug#797507: scikit-learn: FTBFS: IndexError: index 1 is out of bounds for axis 0 with size 1
Chris Lamb
lamby at debian.org
Mon Aug 31 08:13:47 UTC 2015
Source: scikit-learn
Version: 0.16.1-2
Severity: serious
Justification: fails to build from source
User: reproducible-builds at lists.alioth.debian.org
Usertags: ftbfs
X-Debbugs-Cc: reproducible-builds at lists.alioth.debian.org
Dear Maintainer,
scikit-learn fails to build from source in unstable/amd64:
[..]
======================================================================
ERROR:
sklearn.tests.test_common.test_non_meta_estimators('KernelRidge',
<class 'sklearn.kernel_ridge.KernelRidge'>)
----------------------------------------------------------------------
Traceback (most recent call last):
File "/usr/lib/python2.7/dist-packages/nose/case.py", line 197, in
runTest
self.test(*self.arg)
File
"/tmp/buildd/scikit-learn-0.16.1/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/utils/estimator_checks.py",
line 174, in check_dtype_object
estimator.fit(X, y.astype(object))
File
"/tmp/buildd/scikit-learn-0.16.1/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/kernel_ridge.py",
line 158, in fit
copy)
File
"/tmp/buildd/scikit-learn-0.16.1/debian/python-sklearn/usr/lib/python2.7/dist-packages/sklearn/linear_model/ridge.py",
line 145, in _solve_cholesky_kernel
overwrite_a=False)
File "/usr/lib/python2.7/dist-packages/scipy/linalg/basic.py", line
77, in solve
b1 = _asarray_validated(b, check_finite=check_finite)
File "/usr/lib/python2.7/dist-packages/scipy/_lib/_util.py", line
140, in _asarray_validated
raise ValueError('object arrays are not supported')
ValueError: object arrays are not supported
----------------------------------------------------------------------
Ran 4092 tests in 139.350s
FAILED (SKIP=19, errors=6)
erion='aic', eps=2.2204460492503131e-16,
fit_intercept=True, max_iter=5, normalize=True,
precompute='auto',
verbose=False)
LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1,
normalize=False)
LinearSVR(C=1.0, dual=True, epsilon=0.0, fit_intercept=True,
intercept_scaling=1.0, loss='epsilon_insensitive', max_iter=20,
random_state=0, tol=0.0001, verbose=0)
MultiTaskElasticNet(alpha=0.01, copy_X=True, fit_intercept=True,
l1_ratio=0.5,
max_iter=5, normalize=False, random_state=0,
selection='cyclic',
tol=0.0001, warm_start=False)
MultiTaskElasticNetCV(alphas=None, copy_X=True, cv=None, eps=0.001,
fit_intercept=True, l1_ratio=0.5, max_iter=5, n_alphas=100,
n_jobs=1, normalize=False, random_state=0,
selection='cyclic',
tol=0.0001, verbose=0)
MultiTaskLasso(alpha=0.01, copy_X=True, fit_intercept=True,
max_iter=5,
normalize=False, random_state=0, selection='cyclic',
tol=0.0001,
warm_start=False)
MultiTaskLassoCV(alphas=None, copy_X=True, cv=None, eps=0.001,
fit_intercept=True, max_iter=5, n_alphas=100, n_jobs=1,
normalize=False, random_state=0, selection='cyclic',
tol=0.0001,
verbose=False)
NuSVR(C=1.0, cache_size=200, coef0=0.0, degree=3, gamma=0.0,
kernel='rbf',
max_iter=-1, nu=0.5, shrinking=True, tol=0.001, verbose=False)
OrthogonalMatchingPursuit(fit_intercept=True, n_nonzero_coefs=None,
normalize=True, precompute='auto', tol=None)
OrthogonalMatchingPursuitCV(copy=True, cv=None, fit_intercept=True,
max_iter=None, n_jobs=1, normalize=True, verbose=False)
PLSRegression(copy=True, max_iter=5, n_components=2, scale=True,
tol=1e-06)
PassiveAggressiveRegressor(C=0.01, class_weight=None, epsilon=0.1,
fit_intercept=True, loss='epsilon_insensitive',
n_iter=5,
random_state=0, shuffle=True, verbose=0,
warm_start=False)
RadiusNeighborsRegressor(algorithm='auto', leaf_size=30,
metric='minkowski',
metric_params=None, p=2, radius=1.0, weights='uniform')
RandomForestRegressor(bootstrap=True, criterion='mse', max_depth=None,
max_features='auto', max_leaf_nodes=None,
min_samples_leaf=1,
min_samples_split=2, min_weight_fraction_leaf=0.0,
n_estimators=5, n_jobs=1, oob_score=False, random_state=0,
verbose=0, warm_start=False)
Ridge(alpha=0.01, copy_X=True, fit_intercept=True, max_iter=None,
normalize=False, solver='auto', tol=0.001)
RidgeCV(alphas=array([ 0.1, 1. , 10. ]), cv=None,
fit_intercept=True,
gcv_mode=None, normalize=False, scoring=None,
store_cv_values=False)
SGDRegressor(alpha=0.01, average=False, epsilon=0.1, eta0=0.01,
fit_intercept=True, l1_ratio=0.15, learning_rate='invscaling',
loss='squared_loss', n_iter=5, penalty='l2', power_t=0.25,
random_state=0, shuffle=True, verbose=0, warm_start=False)
SVR(C=1.0, cache_size=200, coef0=0.0, degree=3, epsilon=0.1,
gamma=0.0,
kernel='rbf', max_iter=-1, shrinking=True, tol=0.001, verbose=False)
TheilSenRegressor(copy_X=True, fit_intercept=True, max_iter=5,
max_subpopulation=100, n_jobs=1, n_subsamples=None,
random_state=0, tol=0.001, verbose=False)
debian/rules:45: recipe for target 'python-test2.7' failed
make[1]: *** [python-test2.7] Error 1
make[1]: Leaving directory '/tmp/buildd/scikit-learn-0.16.1'
debian/rules:23: recipe for target 'binary' failed
make: *** [binary] Error 2
dpkg-buildpackage: error: fakeroot debian/rules binary gave error exit
status 2
[..]
The full build log is attached or can be viewed here:
https://reproducible.debian.net/logs/unstable/amd64/scikit-learn_0.16.1-2.build1.log.gz
Regards,
--
,''`.
: :' : Chris Lamb
`. `'` lamby at debian.org / chris-lamb.co.uk
`-
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